Kinship Testing


In kinship testing, the level of genotypic similarity between individuals at suitable genetic markers is utilised to assess their degree of familial relationship. Inference is made either by evaluating the available genotypic information under different hypotheses about kinship, represented by different pedigree structures, or through the direct quantification of kinship coefficients using a statistical approach. One complicating factor in genetic kinship testing, however, is population structure whereby the assumption of statistical independence between genotypes of unrelated individuals, or between genotypes at unlinked markers, may become violated. Similarly, physical linkage between markers can also render the simple, so‐called ‘product rule’ for composite likelihood calculation invalid. In these instances, specific statistical approaches and computer algorithms need to be used that properly adjust the relevant computations for population structure or linkage.

Key Concepts:

  • Kinship testing involves evaluation of the level of genotypic similarity between individuals at selected genetic markers.

  • The optimal basis for decision‐making in kinship testing is the likelihood ratio of different hypotheses about kinship, taking the available genetic evidence into account.

  • Posterior odds for given hypotheses about kinship can be calculated from prior odds and likelihood ratios, using Bayes formula.

  • Coancestry as arising, for example, from population structuring renders the calculation of multimarker likelihoods by simple multiplication of single‐marker likelihoods (known as the ‘product rule’) invalid.

  • General kinship testing is equivalent to likelihood calculations of genotypes in pedigrees, which in turn must take physical linkage between markers into account.

  • The degree of kinship between two individuals is quantified by their coefficient of relatedness, which equals the expected number of alleles shared identical‐by‐descent from a common ancestor.

  • The coefficient of relatedness is either calculated for a given type of relationship, and then used to predict genetic similarity, or estimated from observed levels of genetic similarity to infer pair‐wise relatedness.

Keywords: kinship; relatedness; paternity; decision‐making

Figure 1.

An example illustrating general kinship testing where the two hypotheses involved are H0: a boy (6) is the son of his social father (4) and H1: the same boy stems from a contact of his grandfather (1) and mother (3).



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Further Reading

Carmichael T and Kuklin A (2000) How to DNA Test Our Family Relationships. Mountain View, CA: AceN Press.

Committee on DNA Forensic Science (1996) The Evaluation of Forensic DNA Evidence. Washington, DC: National Academy Press.

Evett IW and Weir BS (1998) Interpreting DNA Evidence. Sunderland, MA: Sinauer Associates.

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Krawczak M (1999) Statistical inference from DNA evidence. In: Epplen JT and Lubjuhn T (eds) A Laboratory Guide to DNA Fingerprinting/Profiling, pp. 229–244. Basel, Switzerland: Birkhäuser.

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Weir BS (1996) Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sunderland, MA: Sinauer Associates.

Weir BS, Anderson AD and Hepler AB (2006) Genetic relatedness analysis: modern data and new challenges. Nature Reviews. Genetics 7: 771–780.

Web Links

CERVUS. A software package that uses a maximum‐likelihood approach to parentage inference. It tackles the problem of typing errors (including null alleles and mutations), takes multiple candidate parents – which may not all be sampled – into account, and provides estimates of the confidence in parentage for the most likely parents

DNAVIEW. A commercial software package for various types of DNA identification analyses, including biostatistical analysis, allele/fragment size databasing, molecular sizing, and other features. Its kinship facility solves irregular cases, including deficiency and incest http://dna‐

EASYPAT. A computer program that allows calculation of likelihood ratios for single locus data, comparing specific types of simple hypotheses regarding the familial relationships involved. Currently, these hypotheses include: (i) parenthood versus non‐parenthood in duos comprising parent and child; (ii) paternity versus non‐paternity in trios comprising mother, child and alleged father; and (iii) full siblings versus half‐siblings in trios comprising one parent and two children http://www.uni‐

NEWPAT. A generalized paternity program that calculates allele frequencies, checks for the presence of non‐amplifying alleles, assays each input file for duplicate entries, searches for parent–offspring relationships according to user‐inputted criteria, and then uses a randomization approach to assess the significance of any matches found

RELATEDNESS and KINSHIP. RELATEDNESS calculates average genetic relatedness among sets of individuals defined by demographic variables, either on average or by pairs. It finds standard errors and confidence intervals for significance testing using a jack‐knife resampling method. KINSHIP is a program that performs maximum likelihood tests of pedigree relationships between pairs of individuals in a population. The program uses genotype information for single‐locus, codominant genetic markers (such as DNA microsatellite loci)

VITESSE. A software package that computes likelihoods of genotype data observed, in pedigrees of different structure. The program uses novel algorithms of set‐recoding and fuzzy inheritance to reduce the number of genotypes needed for exact computation of the likelihood, which accelerates the calculation. It also represents multilocus genotypes locus‐by‐locus to reduce the memory requirements http://www‐

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How to Cite close
Krawczak, M(Sep 2010) Kinship Testing. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0005453.pub2]